
def generate_L(data_set, k, min_support):
    support_data = {}
    C1 = create_C1(data_set)
    print('number of candicate 1-itemset', len(C1))
    
    L1 = generate_Lk_by_Ck(data_set, C1, min_support, support_data)
    print('number of frequent 1-itemset', len(L1))
    #print(L1)
    
    Lksub1 = L1.copy()
    L = []
    L.append(Lksub1)
    for i in range(2, k + 1):
        Ci = create_Ck(Lksub1, i)
        print('number of candicate %d-itemset %d' %(i, len(Ci)))
        #print(Ci)
        
        Li = generate_Lk_by_Ck(data_set, Ci, min_support, support_data)
        print('number of frequent %d-itemset %d' %(i, len(Li)))
        
        Lksub1 = Li.copy()
        L.append(Lksub1)
        
    return L, support_data


def generate_Lk_by_Ck(data_set, Ck, min_support, support_data):
    Lk = set()
    item_count = {}
    for t in data_set:
        for item in Ck:
            if item.issubset(t):
                if item not in item_count:
                    item_count[item] = 1
                else:
                    item_count[item] += 1
    t_num = float(len(data_set))
    for item in item_count:
        if (item_count[item] / t_num) >= min_support:
            Lk.add(item)
            support_data[item] = item_count[item] / t_num
    return Lk


def create_C1(data_set):
    C1 = set()
    for t in data_set:
        for item in t:
            item_set = frozenset([item])
            C1.add(item_set)
    return C1


def create_Ck(Lksub1, k):
    Ck = set()
    len_Lksub1 = len(Lksub1)
    list_Lksub1 = list(Lksub1)
    for i in range(len_Lksub1):
        for j in range(i, len_Lksub1):
            l1 = list(list_Lksub1[i])
            l2 = list(list_Lksub1[j])
            l1.sort()
            l2.sort()
            if l1[0:k-2] == l2[0:k-2]:
                Ck_item = list_Lksub1[i] | list_Lksub1[j]
                # pruning
                if filter(Ck_item, Lksub1):
                    Ck.add(Ck_item)
    return Ck


def generate_big_rules(L, support_data, min_conf):
    big_rule_list = []
    sub_set_list = []
    for i in range(0, len(L)):
        for freq_set in L[i]:
            for sub_set in sub_set_list:
                if sub_set.issubset(freq_set):
                    conf = support_data[freq_set] / support_data[freq_set - sub_set]
                    big_rule = (freq_set - sub_set, sub_set, conf)
                    if conf >= min_conf and big_rule not in big_rule_list:
                        big_rule_list.append(big_rule)
            sub_set_list.append(freq_set)
    return big_rule_list


def filter(Ck_item, Lksub1):
    for item in Ck_item:
        sub_Ck = Ck_item - frozenset([item])
        if sub_Ck not in Lksub1:
            return False
    return True



data_set = [['Milk', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],
            ['Dill', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],
            ['Milk', 'Apple', 'Kidney Beans', 'Eggs'],
            ['Milk', 'Unicorn', 'Corn', 'Kidney Beans', 'Yogurt'],
            ['Corn', 'Onion', 'Onion', 'Kidney Beans', 'Ice cream', 'Eggs']]

L, support_data = generate_L(data_set, k=3, min_support=0.6)

big_rules_list = generate_big_rules(L, support_data, min_conf=0.7)
for i in range(len(L)):
    if L[i] != set():
        print('频繁{}项集:'.format(i + 1), L[i])
print('关联规则：')
for item in big_rules_list:
    print(item[0], "=>", item[1], "conf(置信度): ", item[2])